Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-Dimensional Embedding with Extra Information
Discrete & Computational Geometry
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In this work we experimentally analyze ensemble algorithms based on Random Subspace and Random Plus-Minus-One Projection, comparing them to the results obtained in literature by the application of Bagging and BagBoosting on the same data sets used in our experiments: Colon and Leukemia. In this work we concentrate on the application of random projection (Badoiu et al., 2006) ensemble of SVMs, with the aim to improve the accuracy of classification, both through SVMs that represent the state-of-the-art in gene expression data analysis (Vapnik, 1998) (Pomeroy et al., 2002), and through the ensemble methods, used in our work to enhance the classification accuracy and capability. Ensemble methods, in fact, train multiple classifiers and combine them to reduce the generalization error of the multi-classifiers system. To make possible the comparison of our results with those obtained in literature by the application of Bagging and BagBoosting, in this works we concentrate on SVMs with linear kernel.